def plot_example(missed, acknowledged):
sensor_miss = import_sensorfile(missed)
sensor_ack = import_sensorfile(acknowledged)
# Window data
mag_miss = window_data(process_input(sensor_miss))
mag_ack = window_data(process_input(sensor_ack))
# Window data
mag_miss = window_data(process_input(sensor_miss))
mag_ack = window_data(process_input(sensor_ack))
# Filter setup
kernel = 15
# apply filter
mag_miss_filter = sci.medfilt(mag_miss, kernel)
mag_ack_filter = sci.medfilt(mag_ack, kernel)
# calibrate data
mag_miss_cal = mf.calibrate_median(mag_miss)
mag_miss_cal_filter = mf.calibrate_median(mag_miss_filter)
mag_ack_cal = mf.calibrate_median(mag_ack)
mag_ack_cal_filter = mf.calibrate_median(mag_ack_filter)
# PLOT
sns.set_style("white")
current_palette = sns.color_palette('muted')
sns.set_palette(current_palette)
plt.figure(0)
# Plot RAW missed and acknowledged reminders
ax1 = plt.subplot2grid((2, 1), (0, 0))
plt.ylim([-1.5, 1.5])
plt.ylabel('Acceleration (g)')
plt.plot(mag_miss_cal, label='Recording 1')
plt.legend(loc='lower left')
ax2 = plt.subplot2grid((2, 1), (1, 0))
# Plot Missed Reminder RAW
plt.ylim([-1.5, 1.5])
plt.ylabel('Acceleration (g)')
plt.xlabel('t (ms)')
plt.plot(mag_ack_cal, linestyle='-', label='Recording 2')
plt.legend(loc='lower left')
# CALC AND SAVE STATS
stats_one = sp.calc_stats_for_data_stream_as_dictionary(mag_miss_cal)
stats_two = sp.calc_stats_for_data_stream_as_dictionary(mag_ack_cal)
data = [stats_one, stats_two]
write_to_csv(data, 'example_waves')
plt.show()
def plot_kernal_length_experiment(missed, acknowledged):
sensor_miss = import_sensorfile(missed)
sensor_ack = import_sensorfile(acknowledged)
# Window data
mag_miss = window_data(process_input(sensor_miss))
mag_ack = window_data(process_input(sensor_ack))
# Filter setup
difference = []
stats_output = []
for num in range(3, 63):
# check if odd
if num % 2 != 0:
kernel = num
# apply filter
mag_miss_filter = sci.medfilt(mag_miss, kernel)
mag_ack_filter = sci.medfilt(mag_ack, kernel)
# calibrate data
mag_miss_cal = mf.calibrate_median(mag_miss)
mag_miss_cal_filter = mf.calibrate_median(mag_miss_filter)
mag_ack_cal = mf.calibrate_median(mag_ack)
mag_ack_cal_filter = mf.calibrate_median(mag_ack_filter)
# STATS
# Calculate the stats for raw and windowed for each
stats_miss = sp.calc_stats_for_data_stream_as_dictionary(mag_miss_cal)
stats_miss_filter = sp.calc_stats_for_data_stream_as_dictionary(mag_miss_cal_filter)
stats_ack = sp.calc_stats_for_data_stream_as_dictionary(mag_ack_cal)
stats_ack_filter = sp.calc_stats_for_data_stream_as_dictionary(mag_ack_cal_filter)
stats_data = [stats_miss, stats_miss_filter, stats_ack, stats_ack_filter]
[data.pop("med", None) for data in stats_data]
print('Stats Missed:' + str(stats_miss))
print('Stats Acknowledged: ' + str(stats_ack))
print('Stats Missed Filtered: ' + str(stats_miss_filter))
print('Stats Acknowledged Filtered:' + str(stats_ack_filter))
# Calculate the percentage difference between the values
dif_stats_raw = calculate_difference(stats_miss, stats_ack)
dif_stats_filtered = calculate_difference(stats_miss_filter, stats_ack_filter)
print('Difference in RAW as percentage:' + str(dif_stats_raw))
print('Difference in FILTERED as percentage:' + str(dif_stats_filtered))
dif_stats_raw_overall = calcualate_meanfordictionary(dif_stats_raw)
dif_stats_filtered_overall = calcualate_meanfordictionary(dif_stats_filtered)
print('Avg. Difference RAW: ' + str(dif_stats_raw_overall))
print('Avg. Difference Filtered: ' + str(dif_stats_filtered_overall))
difference.append((kernel, dif_stats_filtered_overall))
if kernel == 15:
stats_output.append(stats_miss)
stats_output.append(stats_ack)
stats_output.append(stats_miss_filter)
stats_output.append(stats_ack_filter)
stats_output.append(dif_stats_raw)
stats_output.append(dif_stats_filtered)
write_to_csv(stats_output)
x_val = [x[0] for x in difference]
y_val = [x[1] for x in difference]
xticks = [str(x) for x in x_val]
base_val = [54] * 63
plt.xticks(x_val, xticks)
plt.xlabel('Window Length (k)')
plt.ylabel('Percentage Difference (%)')
plt.ylim([40, 140])
plt.plot(x_val, y_val, label='Median Filter')
plt.plot(base_val, linestyle='--', label='Baseline (Unfiltered)')
plt.legend(loc='lower right')
plt.show()